It is known to monitor the condition of an apparatus or process for the purposes of reducing or minimising the risk of failure of the apparatus or process and/or to optimise the performance of the monitored apparatus or process. Known conditioning monitoring methods generally involve the repeated measurement of one or more variables associated with the monitored apparatus or process and/or an environment surrounding the monitored apparatus or process over a period of time. The one or more measured variables may vary in response to a change in a condition of the monitored apparatus or process. Additionally or alternatively, the one or more measured variables may vary in response to a change in one or more environmental variables. For example, the one or more measured variables may vary in response to a change in the ambient temperature and/or pressure. Known condition monitoring methods may be used to identify a change in a condition of the monitored apparatus or process even in the presence of a change in one or more environmental variables. Such known condition monitoring methods generally rely on the calculation of a vector of predicted or estimated variable values and a comparison of the predicted variable values with current variable values to identify a change in a condition of the monitored apparatus or process. However, calculating such predicted variable values may be complex and may be difficult to implement. In addition, the method of calculating the predicted vector may not be appropriate for some applications. For example, the method of calculating the predicted vector may be too slow, too inaccurate and or may lack sufficient reliability for some applications.
US2008/0183425 discloses an apparatus and method for monitoring the condition of a power plant in which a non-parametric empirical model is constructed from historical sensor readings to predict or estimate current sensor readings. The predicted sensor readings are compared with current sensor readings to identify changes in the state of the power plant. More specifically, a plurality of memory vectors is provided, wherein each memory vector comprises a set of historical readings taken from a plurality of sensors at a different instant in time. A query vector is provided comprising a current reading from each sensor. A distance between the query vector and each of the memory vectors is determined and a weight is evaluated for each memory vector according to the proximity of the query vector to the memory vector. The weights are subsequently used to determine a vector of predicted sensor readings based on a locally weighted regression. The vector of predicted sensor readings and the query vector are compared to identify changes in the condition of the power plant.